Project description:Professional fact-checkers and fact-checking organizations provide a critical public service. Skeptics of modern media, however, often question the accuracy and objectivity of fact-checkers. The current study assessed agreement among two independent fact-checkers, The Washington Post and PolitiFact, regarding the false and misleading statements of then President Donald J. Trump. Differences in statement selection and deceptiveness scaling were investigated. The Washington Post checked PolitiFact fact-checks 77.4% of the time (22.6% selection disagreement). Moderate agreement was observed for deceptiveness scaling. Nearly complete agreement was observed for bottom-line attributed veracity. Additional cross-checking with other sources (Snopes, FactCheck.org), original sources, and with fact-checking for the first 100 days of President Joe Biden's administration were inconsistent with potential ideology effects. Our evidence suggests fact-checking is a difficult enterprise, there is considerable variability between fact-checkers in the raw number of statements that are checked, and finally, selection and scaling account for apparent discrepancies among fact-checkers.
Project description:Human disease is heterogeneous, with similar disease phenotypes resulting from distinct combinations of genetic and environmental factors. Small-molecule profiling can address disease heterogeneity by evaluating the underlying biologic state of individuals through non-invasive interrogation of plasma metabolite levels. We analyzed metabolite profiles from an oral glucose tolerance test (OGTT) in 50 individuals, 25 with normal (NGT) and 25 with impaired glucose tolerance (IGT). Our focus was to elucidate underlying biologic processes. Although we initially found little overlap between changed metabolites and preconceived definitions of metabolic pathways, the use of unbiased network approaches identified significant concerted changes. Specifically, we derived a metabolic network with edges drawn between reactant and product nodes in individual reactions and between all substrates of individual enzymes and transporters. We searched for "active modules"--regions of the metabolic network enriched for changes in metabolite levels. Active modules identified relationships among changed metabolites and highlighted the importance of specific solute carriers in metabolite profiles. Furthermore, hierarchical clustering and principal component analysis demonstrated that changed metabolites in OGTT naturally grouped according to the activities of the System A and L amino acid transporters, the osmolyte carrier SLC6A12, and the mitochondrial aspartate-glutamate transporter SLC25A13. Comparison between NGT and IGT groups supported blunted glucose- and/or insulin-stimulated activities in the IGT group. Using unbiased pathway models, we offer evidence supporting the important role of solute carriers in the physiologic response to glucose challenge and conclude that carrier activities are reflected in individual metabolite profiles of perturbation experiments. Given the involvement of transporters in human disease, metabolite profiling may contribute to improved disease classification via the interrogation of specific transporter activities.
Project description:BackgroundMicroarray experiments in mice have shown that high fat diet can lead to elevated expression of genes that are disproportionately associated with immune functions. These effects of high fat (atherogenic) diet may be due to infiltration of tissues by leukocytes in coordination with inflammatory processes.Methodology/principal findingsThe Novartis strain-diet-sex microarray database (GSE10493) was used to evaluate the hepatic effects of high fat diet (4 weeks) in 12 mouse strains and both genders. We develop and apply an algorithm that identifies "signature transcripts" for many different leukocyte populations (e.g., T cells, B cells, macrophages) and uses this information to derive an in silico "inflammation profile". Inflammation profiles highlighted monocytes, macrophages and dendritic cells as key drivers of gene expression patterns associated with high fat diet in liver. In some strains (e.g., NZB/BINJ, B6), we estimate that 50-60% of transcripts elevated by high fat diet might be due to hepatic infiltration by these cell types. Interestingly, DBA mice appeared to exhibit resistance to localized hepatic inflammation associated with atherogenic diet. A common characteristic of infiltrating cell populations was elevated expression of genes encoding components of the toll-like receptor signaling pathway (e.g., Irf5 and Myd88).Conclusions/significanceHigh fat diet promotes infiltration of hepatic tissue by leukocytes, leading to elevated expression of immune-associated transcripts. The intensity of this effect is genetically controlled and sensitive to both strain and gender. The algorithm developed in this paper provides a framework for computational analysis of tissue remodeling processes and can be usefully applied to any in vivo setting in which inflammatory processes play a prominent role.
Project description:Misleading graphs are a source of misinformation that worry many experts. Especially people with a low graph literacy are thought to be persuaded by graphs that misrepresent the underlying data. But we know little about how people interpret misleading graphs and how these graphs influence their opinions. In this study we focus on the effect of truncating the y-axis for a line chart which exaggerates an upgoing trend. In a randomized controlled trial, we showed participants either a normal or a misleading chart, and we did so in two different contexts. After they had seen the graphs, we asked participants their opinion on the trend and to give an estimation of the increase. Finally we measured their graph literacy. Our results show that context is the only significant factor in opinion-forming; the misleading graph and graph literacy had no effect. None of these factors had a significant impact on estimations for the increase. These results show that people might be less susceptible to misleading graphs than we thought and that context has more impact than a misleading y-axis.
Project description:The correct interpretation of copy number gains in patients with developmental delay and multiple congenital anomalies is hampered by the large number of copy number variations (CNVs) encountered in healthy individuals. The variable phenotype associated with copy number gains makes interpretation even more difficult. Literature shows that inheritence, size and presence in healthy individuals are commonly used to decide whether a certain copy number gain is pathogenic, but no general consensus has been established. We aimed to develop guidelines for interpreting gains detected by array analysis using array CGH data of 300 patients analysed with the 105K Agilent oligo array in a diagnostic setting. We evaluated the guidelines in a second, independent, cohort of 300 patients. In the first 300 patients 797 gains of four or more adjacent oligonucleotides were observed. Of these, 45.4% were de novo and 54.6% were familial. In total, 94.8% of all de novo gains and 87.1% of all familial gains were concluded to be benign CNVs. Clinically relevant gains ranged from 288 to 7912 kb in size, and were significantly larger than benign gains and gains of unknown clinical relevance (P < 0.001). Our study showed that a threshold of 200 kb is acceptable in a clinical setting, whereas heritability does not exclude a pathogenic nature of a gain. Evaluation of the guidelines in the second cohort of 300 patients revealed that the interpretation guidelines were clear, easy to follow and efficient.
Project description:The Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is an insect-pathogen baculovirus. In this study, we applied the Oxford Nanopore Technologies platform for the analysis of the polyadenylated fraction of the viral transcriptome using both cDNA and direct RNA sequencing methods. We identified and annotated altogether 132 novel transcripts and transcript isoforms, including 4 coding and 4 non-coding RNA molecules, 47 length variants, 5 splice isoforms, as well as 23 polycistronic and 49 complex transcripts. All of the identified novel protein-coding genes were 5'-truncated forms of longer host genes. In this work, we demonstrated that in the case of transcript start site isoforms, the promoters and the initiator sequence of the longer and shorter variants belong to the same kinetic class. Long-read sequencing also revealed a complex meshwork of transcriptional overlaps, the function of which needs to be clarified. Additionally, we developed bioinformatics methods to improve the transcript annotation and to eliminate the non-specific transcription reads generated by template switching and false priming.
Project description:Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge.
Project description:The product of the vlf-1 (very late factor 1) gene is required for expression of very late genes during the final phase of infection. To determine whether VLF-1 functions as a transcriptional activator, VLF-1 was overexpressed and purified by affinity and cation exchange chromatography. The addition of purified protein to transcription assays containing baculovirus RNA polymerase stimulated transcription of the very late polyhedrin promoter but not the late 39k promoter. Furthermore, construction and analysis of chimeric templates identified sequences within the polyhedrin promoter that were necessary for enhancement.
Project description:The pfam04002 annotation describes RadC as a bacterial DNA repair protein. Although the radC gene is expressed specifically during competence for genetic transformation in Streptococcus pneumoniae, we report that radC mutants exhibit normal uptake and processing of transforming DNA. They also display normal sensitivity to DNA-damaging agents, providing no support for the rad epithet.
Project description:Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.